We are seeking a Research Scientist specialising in computer vision and multimodal AI to join a leading research and development team

Key Responsibilities

Advanced Model Research

• Design and develop Vision Transformer and multimodal large-model architectures with improved reasoning, efficiency, and scalability.
• Advance multimodal alignment, representation learning, and long-context modelling.
• Research scalable training techniques for large multimodal models.
• Improve model architectures to strengthen generalisation, robustness, and overall performance.
• Explore new approaches across multimodal understanding, generation, and reasoning.

Multimodal Data Development

• Process large-scale multimodal datasets spanning images, video, audio, and text.
• Build data pipelines for cleaning, filtering, annotation, validation, and quality control.
• Construct and maintain reproducible datasets with clear versioning and documentation.
• Optimise data mixtures, sampling methods, and curriculum strategies for model training.
• Improve dataset quality through evaluation results and feedback-driven curation.
• Develop methods for identifying low-quality, duplicated, biased, or uninformative data.

Large Multimodal Model Systems

• Build and improve distributed training systems for large-scale multimodal models.
• Optimise GPU utilisation, cluster efficiency, resource allocation, and workload scheduling.
• Develop scalable training frameworks and reusable research infrastructure.
• Engineer training, inference, evaluation, and serving systems.
• Improve the scalability, reliability, stability, and performance of model development pipelines.
• Work closely with infrastructure and platform teams to resolve system-level bottlenecks.

Research Application and Delivery

• Apply multimodal capabilities to intelligent assistants, content generation, and related AI applications.
• Translate research outcomes into production-ready systems and user-facing features.
• Collaborate with product, engineering, and research teams to deploy, evaluate, and iterate models.
• Communicate research findings through technical reports, presentations, publications, and demonstrations.

Essential Requirements

• Bachelor’s degree or higher in Computer Science, Mathematics, Statistics, Engineering, or another relevant technical discipline.
• Strong Python programming skills.
• Hands-on experience with PyTorch or comparable deep learning frameworks.
• Strong ability to develop, implement, and evaluate machine learning algorithms.
• Solid mathematical reasoning and problem-solving skills.
• Good understanding of modern computer vision or multimodal machine learning methods.
• Ability to collaborate effectively across research, engineering, product, and infrastructure teams.
• Strong written and verbal communication skills.
• Self-motivated, resilient, and comfortable working on complex research problems with a high degree of technical uncertainty.

Preferred Qualifications

• Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field.
• Publications at leading artificial intelligence or computer vision conferences, including CVPR, ICCV, ECCV, NeurIPS, ICML, or ICLR.
• Experience pre-training, fine-tuning, or evaluating large-scale vision or multimodal models.
• Experience with Vision Transformers, vision-language models, multimodal large language models, or generative vision systems.
• Familiarity with distributed training, mixed-precision training, model parallelism, or large-scale GPU clusters.
• Contributions to high-impact open-source projects in computer vision, natural language processing, multimodal AI, or machine learning systems.
• Research or internship experience within a recognised technology company, research laboratory, or academic institution.
• Experience translating research prototypes into scalable production systems.

Additional Skills

• Strong understanding of representation learning, attention mechanisms, transformers, and generative modelling.
• Experience working with large multimodal datasets and data-quality pipelines.
• Familiarity with model evaluation, benchmarking, ablation studies, and experiment reproducibility.
• Ability to identify research opportunities and independently drive projects from initial concept to validated outcome.
• Interest in advancing the capabilities, efficiency, and reliability of next-generation multimodal AI systems.

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